A comparision of cell phone driver and druck drive

J O I N T

C E N T E R

AEI-BROOKINGS JOINT CENTER FOR REGULATORY STUDIES

A Comparison of the Cell Phone Driver and the Drunk Driver
David L. Strayer, Frank A Drews, and Dennis J. Crouch*
Working Paper 04-13
July 2004

This paper can be downloaded free of charge from the Social Science Research Network
at: http: //ssrn.com/abstract=570222

*

The authors are David L. Strayer, Frank A. Drews, and Dennis J. Crouch of the University of
Utah. Support for this study was provided through a grant from the Federal Aviation
Administration. We wish to thank the Utah Highway Patrol for providing the breath analyzer and
GE I-SIM for providing access to the driving simulator. Danica Nelson, Amy Alleman, and Joel
Cooper assisted in the data collection. Correspondence concerning this article should be
addressed to David Strayer, Department of Psychology, 380 S. 1530 E. Rm 502, University of
Utah, Salt Lake City, UT 84112, USA (e-mail: ).


J O I N T

C E N T E R

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Massachusetts Institute
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University of Chicago

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Harvard University

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Executive Summary
We used a high-fidelity driving simulator to compare the performance of cell-phone
drivers with drivers who were legally intoxicated from ethanol. When drivers were conversing
on either a hand-held or hands-free cell-phone, their braking reactions were delayed and they
were involved in more traffic accidents than when they were not conversing on the cell phone.
By contrast, when drivers were legally intoxicated they exhibited a more aggressive driving
style, following closer to the vehicle immediately in front of them and applying more force while
braking. When controlling for driving conditions and time on task, cell-phone drivers exhibited
greater impairment than intoxicated drivers. The results have implications for legislation
addressing driver distraction caused by cell phone conversations.


1

A Comparison of the Cell Phone Driver and the Drunk Driver
David L. Strayer, Frank A. Drews, and Dennis J. Crouch

1. Introduction

While often reminded to pay full attention to driving, people regularly engage in a wide
variety of multi-tasking activities when they are behind the wheel. Indeed, as the average time
spent commuting increases, there is a growing interest in trying to make the time spent on the
roadway more productive. Unfortunately, due to the inherent limited capacity of human attention
(e.g., Kanheman, 1973; Navon & Gopher, 1979), engaging in these multi-tasking activities often
comes at a cost of diverting attention away from the primary task of driving. There are a number
of more traditional sources of driver distraction. These “old standards” include talking to
passengers, eating, drinking, lighting a cigarette, applying make-up, listening to the radio, etc.
(Stutts et al., 2003). However, over the last decade many new electronic devices have been
developed and are making their way into the vehicle. In many cases, these new technologies are
engaging, interactive information delivery systems. For example, drivers can now surf the

internet, send and receive e-mail or fax, communicate via cellular device, and even watch
television. There is good reason to believe that some of these new multi-tasking activities may be
substantially more distracting than the old standards because they are more cognitively engaging
and because they are performed over longer periods of time.
The current research focuses on a dual-task activity that is commonly engaged in by over
100 million drivers in the United States: The concurrent use of cell phones while driving (CTIA,
2004, Goodman et. al., 1999). It is now well established that cell phone use impairs the driving
performance of younger adults (Alm & Nilsson, 1995; Briem & Hedman, 1995; Brookhuis, De
Vries, & De Waard, 1991; Brown, Tickner, & Simmonds, 1969; Goodman et. al., 1999;
McKnight & McKnight, 1993; Redelmeier & Tibshirani, 1997; Strayer & Johnston, 2001;
Strayer, Drews, & Johnston, 2003). For example, drivers are more likely to miss critical traffic
signals (e.g., traffic lights, a vehicle braking in front of the driver, etc.), slower to respond to the
signals that they do detect, and more likely to be involved in rear-end collisions when they are
conversing on a cell phone (Strayer, Drews, & Johnston, 2003). In addition, even when


2
participants direct their gaze at objects in the driving environment that they often fail to “see”
them when they are talking on a cell phone because attention has been directed away from the
external environment and towards an internal, cognitive context associated with the phone
conversation. However, what is lacking in the literature is a clear benchmark with which to
evaluate the relative risks associated with this dual-task activity.
In their seminal article, Redelmeier and Tibshirani (1997) reported epidemiological
evidence suggesting that “the relative risk [of being in a traffic accident while using a cellphone] is similar to the hazard associated with driving with a blood alcohol level at the legal
limit” (p. 465). These estimates were made by evaluating the cellular records of 699 individuals
involved in motor vehicle accidents. It was found that 24% of these individuals were using their
cell phone within the 10-minute period preceding the accident, and this was associated with a
four-fold increase in the likelihood of getting into an accident. Moreover, these authors
suggested that the interference associated with cell phone use was due to attentional factors
rather than to peripheral factors such as holding the phone. However, there are several limitations

to this important study. First, while the study established a strong association between cell phone
use and motor vehicle accidents, it did not demonstrate a causal link between cell phone use and
increased accident rates. For example, there may be self-selection factors underlying the
association: People who use their cell phone may be more likely to engage in risky behavior and
this increase in risk taking may be the cause of the correlation. It may also be the case that being
in an emotional state may increase one’s likelihood of driving erratically and may also increase
the likelihood of talking on a cell phone. Finally, limitations on establishing an exact time of the
accident lead to uncertainty regarding the precise relationship between talking on a cell phone
while driving and increased traffic accidents.
If the relative risk estimates of Redelmeier and Tibshirani (1997) can be substantiated in
a controlled laboratory experiment and there is a causal link between cell phone use and
impaired driving, then these data would be of immense importance for public safety and
legislative bodies. Here we report the result of a controlled study that directly compared the
performance of drivers who were conversing on either a hand-held or hands-free cell-phone with
the performance of drivers with a blood alcohol level at the legal limit.
We used a car-following paradigm (see also Alm & Nilsson, 1995; Lee et al., 2001;
Strayer, Drews, & Johnston, 2003) in which participants drove on a multi-lane freeway following


3
a pace car that would brake at random intervals. We measured a number of performance
variables (e.g., driving speed, following distance, brake reaction time, etc.) that have been shown
to affect the likelihood and severity of rear-end collisions, the most common type of traffic
accident reported to police (Brown, Lee, & McGehee, 2001; Lee et al., 2001). Three
counterbalanced conditions were studied: single-task driving (baseline condition), driving while
conversing on a cell-phone (cell-phone condition), and driving with a blood alcohol
concentration of 0.08 wt/vol. (alcohol condition). The driving tasks were performed on a highfidelity driving simulator.

2. Method

Participants
Forty-one adults (26 male and 15 female) participated in the IRB approved study.
Participants ranged in age from 22 to 45, with an average age of 26. All had normal or correctedto-normal vision and a valid driver’s license.

Stimuli and Apparatus
A PatrolSim high-fidelity driving simulator, illustrated in Figure 1 and manufactured by
GE I-Sim, was used in the study. A freeway road database simulated a 24-mile multi-lane
interstate with on and off-ramps, overpasses, and two and three-lane traffic in each direction.
Daytime driving conditions with good visibility and dry pavement were used. A pace car,
programmed to travel in the right-hand lane, braked intermittently throughout the scenario.
Distractor vehicles were programmed to drive between 5% and 10% faster than the pace car in
the left lane, providing the impression of a steady flow of traffic. Unique driving scenarios,
counterbalanced across participants, were used for each condition in the study. Measures of realtime driving performance, including driving speed, distance from other vehicles, and brake
inputs, were sampled at 30 Hz and stored for later analysis. Cellular service was provided by
Sprint PCS. The cell-phone was manufactured by LG Electronics inc. (model TP1100). For
hands-free conditions, a Plantronics M135 headset (with ear piece and boom microphone) was
attached to the cell-phone. Blood alcohol concentration levels were measured using an
intoxilyzer 5000, manufactured by CMI Inc.


4

Procedure
The experiment was conducted in three sessions on different days. The first session
familiarized participants with the driving simulator using a standardized adaptation sequence.
The order of subsequent alcohol and cell-phone sessions was counterbalanced across
participants. In these latter sessions, the participant’s task was to follow the intermittently
braking pace car driving in the right-hand lane of the highway. When the participant stepped on
the brake pedal in response to the braking pace car, the pace car released its brake and
accelerated to normal highway speed. If the participant failed to depress the brake, they would

eventually collide with the pace car. That is, like real highway stop and go traffic, the participant
was required to react in a timely and appropriate manner to a vehicle slowing in front of them.
In the alcohol session, participants drank a mixture of orange juice and vodka (40%
alcohol by volume) calculated to achieve a blood alcohol concentration of 0.08 wt/vol. Blood
alcohol concentrations were verified using infrared spectrometry breath analysis immediately
before and after the alcohol driving condition. Participants drove in the 15-minute car-following
scenario while legally intoxicated.
In the cell-phone session, three counterbalanced conditions were included: single-task
baseline driving, driving while conversing on a hand-held cell phone, and driving while
conversing on a hands-free cell phone. In both cell-phone conditions, the participant and a
research assistant engaged in naturalistic conversations on topics that were identified on the first
day as being of interest to the participant. To minimize interference from manual components of
cell phone use, the call was initiated before participants began driving.

3. Results and Discussion

In order to better understand the differences between conditions, driving profiles were
created by extracting 10 second epochs of driving performance that were time-locked to the
onset of the pace car’s brake lights. We created profiles of the participant’s braking response,
driving speed, and following distance.
Figure 2 presents the braking profiles. In the baseline condition, participants began
braking within 1 second of pace car deceleration. Similar braking profiles were obtained for both


5
the cell phone and alcohol conditions. However, compared to baseline, when participants were
legally intoxicated they tended to brake with greater force, whereas participant’s reactions were
slower when they were conversing on a cell phone.
Figure 3 presents the driving speed profiles. In the baseline condition, participants began
decelerating within 1 second of the onset of the pace car’s brake lights; reaching minimum speed

2 seconds after the pace car began to decelerate, whereupon participants began a gradual return
to pre-braking driving speed. When participants were legally intoxicated, they drove slower, but
the shape of the speed profile did not differ from baseline. By contrast, when participants were
conversing on a cell phone it took them longer to recover their speed following braking.
Figure 4 presents the following distance profiles. In the baseline condition, participants
followed approximately 28 meters behind the pace car and as the pace car decelerated, the
following distance decreased, reaching nadir approximately 2 seconds after the onset of the pace
car’s brake lights. When participants were legally intoxicated, they followed closer to the pace
car, whereas participants increased their following distance when they were conversing on a cell
phone.
Table 1 presents the seven performance variables that were measured to determine how
participants reacted to the vehicle braking in front of them. Brake reaction time is the time
interval between the onset of the pace car’s brake lights and the onset of the participant’s braking
response (i.e., defined as a minimum of 1% depression of the participant’s brake pedal). Braking
force is the maximum force that the participant applied to the brake pedal in response to the
braking pace car (expressed as a percentage of maximum). Speed is the average driving speed of
the participant’s vehicle (expressed in miles per hour). Mean following distance is the distance
prior to braking between the rear bumper of the pace car and the front bumper of the
participant’s car. SD following Distance is the standard deviation of following distance. Halfrecovery time is the time for participants to recover 50% of the speed that was lost during
braking (e.g., if the participant’s car was traveling at 60 MPH before braking and decelerated to
40 MPH after braking, then half recovery time would be time taken for the participant’s vehicle
to return to 50 MPH). Also shown in the table are the total number of collisions in each phase of
the study. We used a Multivariate Analysis of Variance (MANOVA) followed by planned
contrasts (shown in Table 2) to provide an overall assessment of driver performance in each of
the experimental conditions.


6
We performed an initial comparison of driving while using a hand-held versus hands-free
cell-phone. Both hand-held and hands-free cell-phone conversations impaired driving. However,

there were no significant differences in the impairments caused by these two modes of cellular
communication (F(6,35)=1.33, p>.27). Therefore, we collapsed across the hand-held and handsfree conditions for all subsequent analyses reported in this article. The observed similarity
between hand-held and hands-free cell-phone conversations is consistent with earlier work (e.g.,
Patten, Kircher, Ostlund, & Nilsson, 2004; Redelmeier & Tibshirani, 1997; Strayer & Johnston,
2001) and calls into question driving regulations that prohibit hand-held cell-phones and permit
hands-free cell-phones.
MANOVAs indicated that both cell-phone and alcohol conditions differed significantly
from baseline (F(6,35)=8.42, p<.01 and F(6,35)=3.92, p<.01, respectively). When drivers were
conversing on a cell-phone, they were involved in more rear-end collisions, their initial reaction
to vehicles braking in front of them was slowed by 8.8%, and the variability in following
distance increased by 24.5%, relative to baseline. In addition, compared to baseline it took
participants who were talking on the cell phone 14.8% longer to recover the speed that was lost
during braking.
By contrast, when participants were legally intoxicated, neither accident rates, nor
reaction time to vehicles braking in front of the participant, nor recovery of lost speed following
braking differed significantly from baseline. Overall, drivers in the alcohol condition exhibited a
more aggressive driving style. They followed closer to the pace vehicle and braked with 23.4%
more force than in baseline conditions. Most importantly, our study found that accident rates in
the alcohol condition did not differ from baseline; however, the increase in hard braking that we
observed is likely to be predictive of increased accident rates over the long run (e.g., Brown, Lee,
& McGehee, 2001).
The MANOVA also indicated that the cell-phone and alcohol conditions differed
significantly from each other, F(6,35)=5.10, p<.01. When drivers were conversing on a cellphone, they were involved in more rear-end collisions, had a more variable following distance,
and took longer to recover the speed that they had lost during braking than when they were
legally intoxicated. Drivers in the alcohol condition also applied greater braking pressure than
drivers in the cell-phone condition.


7

4. Conclusions

Taken together, we found that both intoxicated drivers and cell-phone drivers performed
differently from baseline, and that the driving profiles of these two conditions differed. Drivers
in the cell-phone condition exhibited a delay in their response to events in the driving scenario
and they were more likely to be involved in a traffic accident. Drivers in the alcohol condition
exhibited a more aggressive driving style, in which they followed closer, necessitating braking
with greater force. With respect to traffic safety, our data are consistent with Redelmeier and
Tibshirani’s (1997) earlier estimates concerning the relative risks of these two activities. In fact,
the data suggest that, when controlling for driving conditions and time on task, cell-phone drivers
may actually exhibit greater impairments (i.e., more accidents and less responsive driving
behavior) than intoxicated drivers. However, the mechanisms underlying the impaired driving
differ. In the case of the cell phone driver, the impairments are due, in large part, to the diversion
of attention from the processing of information necessary for the safe operation of a motor
vehicle (Strayer & Johnston, 2001; Strayer, Drews, & Johnston, 2003). These attention-related
deficits are relatively transient (i.e., occurring while the driver is on the cell phone), whereas the
impairment from alcohol persists for prolonged periods of time.
The objective of the present study was to establish a clear benchmark for assessing the
relative risks associated with using a cell phone while driving. We compared the cell phone
driver with the drunk driver for two reasons. First, there are clear societal norms associated with
intoxicated driving and laws in the United States expressly prohibit driving with a blood alcohol
level at or above .08. Logical consistency would seem to dictate that any activity that leads to
impairments in driving equal to or greater than the drunk driving standard should be avoided.
Second, the epidemiological study by Redelmeier and Tibshirani (1997) suggested that “the
relative risk [of being in a traffic accident while using a cell-phone] is similar to the hazard
associated with driving with a blood alcohol level at the legal limit” (p. 465). The data presented
in this article are consistent with this estimate and indicate that when controlling for driving
conditions and time on task that the impairments associated with using a cell phone while driving
can be as profound as those associated with driving with a blood alcohol level at .08. With
respect to cell phone, clearly the safest course of action is to not use it while driving. However,

8
regulatory issues are best left to legislators who are provided with the latest scientific evidence.
In the long run, skillfully crafted regulation and better driver education addressing driver
distraction will be essential to keep our roadways safe.


9
Table 1. Means and standard errors (in parentheses) for the Alcohol, Baseline, and CellPhone conditions.

Alcohol

Baseline

Cell Phone

Total Accidents

0

0

3

Brake Reaction Time (msec)

786 (33)

806 (43)

877 (45)

Maximum Braking Force

69.6 (3.6)

56.4 (2.5)

55.2 (2.9)

Speed (MPH)

53.0 (1.9)

55.6 (0.6)

54.0 (1.3)

Mean Following Distance (meters)

26.3 (1.7)

27.5 (1.3)

28.6 (1.6)

SD Following Distance (meters)

10.2 (0.6)

9.4 (0.5)

11.7 (0.8)

½ Recovery Time (sec)

5.4 (0.3)

5.4 (0.3)

6.2 (0.4)


10
Table 2. T-test values for the pair-wise comparisons. All comparisons have a 40 degrees of
freedom.

Alcohol
Brake Reaction Time (msec)

Alcohol
Cell Phone

Maximum Braking Force

Speed (MPH)

½ Recovery Time (sec)

0.64
1.34

0.42

1.68†
0.76

1.06

1.13
1.13

1.69†

Alcohol
Cell Phone

† p<.10, * p<.05, ** p<.01

4.24**

Alcohol
Cell Phone

5.43**
4.54**

Alcohol
Cell Phone

SD Following Distance (meters)

2.04*

Alcohol
Cell Phone

Mean Following Distance (meters)

0.45

Alcohol
Cell Phone

Baseline

4.20**
0.09

2.14*

3.71**


11
Figure 1. A participant talking on a cell phone while driving in the GE I-SIM driving
simulator.

12
Figure 2. The braking profile.

% Brake Depression

Braking Profile
20
18
16
14
12
10
8
6
4
2
0

Alcohol
Baseline
Cell Phone

0

1

2

3

Time (sec)

4

5


13
Figure 3. The speed profile.

Speed Profile
58

Speed (MPH)

57
56
55
54
53
Alcohol
Baseline
Cell Phone

52
51
50
0

1

2

3

4

5

6

Time (sec)

7

8

9 10


14
Figure 4. The following distance profile.

Following Distance
31
Distance (Meters)

30
29
28

27
Alcohol
Baseline
Cell Phone

26
25
24
0

1

2

3

4

5

6

Time (sec)

7

8

9 10

15
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